Derin Öğrenme Çerçevesini Kullanarak İnvazif Duktal Karsinom Meme Kanserinin Erken Tanısı

Abstract

Bu çalışma, dünya genelinde kadınlarda yaygın olarak görülen invazif duktal karsinom meme kanseri üzerine odaklanmaktadır. Erken teşhis, hayat kurtarıcı olabilecek bu kanser türü için kritiktir. Çalışmanın amacı, meme kanserinin erken teşhisini belirlemek için derin öğrenme ve görüntü işleme tekniklerini kullanmaktır. Meme kanseri adlı bir veri seti, geleneksel tanı yöntemleriyle değerlendirildiğinde karmaşık veya zaman alıcı olabilen görüntüler içermektedir. Derin öğrenme modelleri, bu zorlukları aşmak için kullanılmıştır. Çalışmada kullanılan modeller, meme kanseri hücrelerini analiz etmiş ve kötü hücrelere sahip olanları kanserli, iyi hücrelere sahip olanları kansersiz olarak sınıflandırmıştır. Beş farklı model (CNN, SVM, Random Forest, DenseNet ve MobileNet) kullanılmıştır. Sonuçlar incelendiğinde, önerilen yöntemin diğer metodlara göre daha iyi performans gösterdiği görülmüştür. Modellerin doğruluk oranları sırasıyla şu şekildedir: CNN (%95,1), SVM (%89,87), Random Forest (%93,21), DenseNet (%94,31) ve MobileNet (%94,6). Bu çalışma, meme kanseri tanısında kullanılacak modeller arasındaki farklılıkları ortaya koymaktadır. Yapay zekanın önemi göz önüne alındığında, bu çalışmanın meme kanseri hastalarının kurtarılmasında önemli bir adım olabileceği öngörülmektedir. Yöntemlerin etkin ve verimli bir şekilde kullanılması durumunda, erken tanı oranının artması ve hastalıkların önlenmesi sağlanabilir.

Keywords

• Yapay Zeka
• Meme Kanseri
• Derin Öğrenme
• Görüntü İşleme
• İnvazif Duktal Karsinom.

Early Diagnosis of Invasive Ductal Carcinoma Breast Cancer using Deep Learning Framework

Abstract

This study is aimed to be conducted on invasive ductal carcinoma breast cancer, which is a type of cancer that is common around the world and found in women. Early diagnosis of this disease can be lifesaving. It was aimed to conduct the study to determine the early diagnosis of breast cancer due to its early detection feature. In addition to deep learning techniques, image processing techniques were also used in the study. A dataset consisting of breast cancer images was used. The images in the data set may be complicated or time-consuming when evaluated using traditional diagnostic methods. This is where deep learning models come into play. The models used in the study analyzed breast cancer cells. As a result of the analysis, cells were classified as cancerous or cancer-free. Five different models were used in this study: CNN, SVM, Random Forest, DenseNet and MobileNet. When the results were examined, it was analyzed that the proposed method showed better performance than other methods. The accuracy rates of the models were: CNN (95.1%), SVM (89.87%), Random Forest (93.21%), DenseNet (94.31%), and MobileNet (94.6%). In conclusion, this study reveals the differences between models used in breast cancer diagnosis. In this period when the importance of artificial intelligence increases, it is predicted that it will be an important step in saving breast cancer patients. If the methods are used efficiently and effectively, the rate of early diagnosis will increase and diseases will be prevented.

Keywords

• Artifical Intelligence
• Breast Canser
• Deep Learning
• Image Processing
• Invasive Ductal Carcinoma.

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